System, method and program product for providing populace centric weather forecasts

ABSTRACT

A populace centric weather forecast system, method of forecasting weather and a computer program product therefor. A forecasting computer applies a grid to a forecast area and provides a weather forecast for each grid cell. Area activity data sources indicate human activity in the forecast area. A dynamic selection module iteratively identifies grid cells for refinement in response to the weather forecast and to indicated/expected human activity. The dynamic selection module provides the forecasting computer with a refined grid for each identified grid cell in each iteration. The forecasting computer provides a refined weather forecast in each iteration.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention is related to providing weather forecasts on widegeographic areas, and more particularly, to tailoring weather forecaststo critical needs of the populace in a wide geographic area.

2. Background Description

Weather forecasts are based on weather data collected from sensors thatare located over a large geographic area or even worldwide. Inforecasting the weather for a wide geographic area, the area typicallyis divided into smaller more manageable units by a superimposing a gridover the area. Then, the relationship of the weather data among theseveral units or grid locations is described in several algebraicequations, e.g., using a Finite Element Model (FEM) for the griddedarea. Frequently, the FEM requires a considerable, even excessive,amount of data processing resources.

Moreover, the higher the grid resolution, the larger the number ofunits, the more complex the FEM equations and, correspondingly, the moredata processing resources consumed in generating weather forecasts. Thedata processing demands may be such that, it may be infeasible toprovide real time or even timely forecasts for all grid locations. Thisis especially troublesome when, as is commonly the case, forecastresults are subject to tight delivery deadlines. What is commonly knownas adaptive mesh refinement (AMR) is a type of dynamic mesh refinementthat has been used to selectively provide real time forecasts.

Adaptive mesh refinement begins with a low resolution grid for an area.The weather map contains coarse-grained cells to provide rough initialforecasts. Where more detailed forecasts are necessary for certaincells, provided there is sufficient data and time available, those cellsare further refined. Typically, refinement is based on quality andquantity of sensors in the area, i.e., focus is on areas with more andbetter sensors. B. Plale et al., “CASA and LEAD: AdaptiveCyberinfrastructure for Real-Time Multiscale Weather Forecasting,” IEEEComputer Magazine, 2006, provides an example of sensor based refinement.Unfortunately, weather for an area is an open, unstable and dynamicsystem and any selected grid may cause have a divergent result. Witheach divergent result, the grid must be redesigned.

However, there is no guarantee that the redesigned grid will converge ona solution. When redesigning the grid in sensor based refinement doesarrive at a solution though, that resulting grid still focuses onweather based the sensors, i.e. sensor quality and quantity. Thus, thefinal grid still may not conform to where actual interest may lie, e.g.,vacation areas in or out of season. The Olympics, for example, arefrequently located in somewhat remote areas, such as Lake Placid, N.Y.in 1932 and 1980. In state of the art approaches the refinement criteriacould either miss important areas or include unimportant areas thatcould be skipped entirely. Thus, sensor based refinement still may failto arrive at an acceptable forecast when, for example, the weather couldimpact people's lives. So, sensor based refinement frequently requiresadditional work, e.g., additional solutions. Occasionally,coarse-grained cells were re-included in the forecast to cover thoseotherwise omitted areas, but without any real improvement over theinitial forecast for those areas.

Thus, there is a need for efficiently providing weather forecasts forlarge areas with a dynamic and unevenly distributed population; and,more particularly for efficiently and quickly arriving at accurateweather forecasts for large populated and unpopulated areas that aredirected to areas where weather may have the most impact on thepopulation.

SUMMARY OF THE INVENTION

A feature of the invention is populace centered weather forecasts;

Another feature of the invention is populace driven grid refinement forweather forecasts;

Yet another feature of the invention is providing weather forecaststailored to the needs of a mobile or fluid population in a large areathat has locally populated and unpopulated areas.

The present invention relates to a populace centric weather forecastsystem, method of forecasting weather and a computer program producttherefor. A forecasting computer applies a grid to a forecast area andprovides a weather forecast for each grid cell. Area activity datasources indicate human activity in the forecast area. A dynamicselection module iteratively identifies grid cells for refinement inresponse to the weather forecast and to indicated/expected humanactivity. The dynamic selection module provides the forecasting computerwith a refined grid for each identified grid cell in each iteration. Theforecasting computer provides a refined weather forecast in eachiteration.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, aspects and advantages will be betterunderstood from the following detailed description of a preferredembodiment of the invention with reference to the drawings, in which:

FIG. 1 shows an example of a populace centric weather forecasting systemaccording to a preferred embodiment of the present invention;

FIG. 2 shows an example of dynamically refining weather forecastsaccording to an embodiment of the present invention;

FIG. 3 shows a simple example of refining a forecast for a wide area,wherein grid refinement is populace centric for local analysis andforecasting according to a preferred embodiment of the presentinvention.

DESCRIPTION OF PREFERRED EMBODIMENTS

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described below with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

Turning now to the drawings and more particularly, FIG. 1 shows anexample of a populace centric weather forecasting system 100 accordingto a preferred embodiment of the present invention. In particular, apreferred system 100 performs cell by cell mesh refinement based on thepotential impact on the cell population, i.e., the human, social andcultural impacts associated with each refined cell. The forecast isbased not just the permanent population, but transient populationpassing through or scheduled to be in the cell. Instead of refining thegrid based on the quantity and quality of sensors in a particular cell,a preferred system 100 selects cells based on the potential impact thatthe weather can have on people in the geographical area within theselected cells during the period being forecast.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

The preferred system 100 includes one or more forecasting computers 102,one in this example, iteratively providing weather forecasts based on arefined grid provided by a dynamic selection module 104. The dynamicselection module 104 generates the refined grid from collected eventdata 106, demographic data 108, real-time incident 110 and historicalincident data 112, forecast data 114 including real-time sensor data116, and geographical data from a Geodatabase 118.

The preferred dynamic selection module 104 uses the available data toselectively refine the current grid based on the potential impact of theweather forecasts 114 to the local populace. The preferred dynamicselection module 104 passes each refined grid to the forecastingcomputer 102, which revises the forecast for the area in the refinedgrid based on meteorological data 114, real-time sensor data 116, anddata from a Geodatabase 118 to. As the refined forecast area diminisheswith each iteration, the data considered in each iteration alsodiminishes to rapidly converge on a populace centric forecast for theentire forecast area, a forecast tailored to the needs and concerns ofthe local populace.

Event data 106 includes real-time data from community and social eventsand scheduled and projected future events. Examples of such eventsinclude sporting events such as games and races, concerts, shows,parties, and corteges such as funeral processions. Demographic data 108includes population density information about specific global areascovered by the forecast system 102. Real-time incident 110 andhistorical incidents data 112 include data on events such as trafficjams, floods, mudslides, hurricanes, accidents and other incidentsreported by agencies and citizens to control centers. Forecast data 114includes current and previous forecasts provided by forecast system 102.The real-time sensor data 116 includes weather sensor information, suchas rain, snow and sleet cloud data taken, for example, from weathersatellites, rain gauges, weather sensors, weather radar and any othersuitable weather collecting sensors. The Geodatabase 118 holds high, midand low resolution maps of the terrain related to the areas covered bythe forecast system 102. Each of the historic/scheduled data collections106, 108, 112, 118 may be maintained current, for example, bysubscription to a Really Simple Syndication (RSS) event distributionservice. Alternately, the computer storing the particular datacollection 106, 108, 112, 118 may periodically update stored data.

In each iteration, the dynamic selection module 104 examines theforecast each cell, marking those cells as complete where cell weatherand demographic conditions do not warrant further consideration. Themarked areas are removed from further consideration and only theremaining cell areas are considered in providing a more comprehensiveforecast. For each of those remaining cells, the dynamic selectionmodule 104 generates a tighter grid and passes the new grid to theforecast computer 102.

Essentially, this winnowing begins with identifying cells with goodweather, balmy or storm free, posing no risk regardless of areapopulation. The dynamic selection module 104 marks those cells complete.

Then, the dynamic selection module 104 identifies any cells with littleor no population and none expected during the forecast period.Identifying cells with little or no population may include checkingnormally uninhabited areas for transients, e.g., active cellulartelephones in or moving through the area. If there are no staticdemographic concentrations (cities, towns, villages, neighborhoods,etc.) or transients in a cell, the dynamic selection module 104 checksfor scheduled activity (social, community or human events) that willdraw transients during the forecast period. So, the dynamic selectionmodule 104 also determines from the event data 106, if the cell has anyevents scheduled for the forecast period. The dynamic selection module104 marks as complete any cells that have sparse or no population(static or transient); or with population, but that are identified asfree from real-time incidents 110 occurring (e.g., in progress) duringthe forecast period or that are historically free from of weatherinduced incidents 112.

The dynamic selection module 104 eliminates the corresponding markedareas from further refinement to shrink the forecast area for subsequentiterations. Each remaining cell encloses an area with conditions thatprevent marking it complete, and the dynamic selection module 104generates a tighter grid for that area. The forecast computer 102 usesthe tighter grid with a higher resolution map for each such area torefine the forecast for those cells.

It should be noted that event data 106, demographic data 108, historicalincident data 112, forecast data 114, and Geodatabase 118 may beseparately collected and maintained in individual computers or PCs or ina single computer or server, e.g., a forecasting computer 102. Also, anyone or more of the data sources 106, 108, 110, 112, 114, 116, 118 may becontained in a single storage on a single computer or distributed inmultiple such storage locations on separate individual computers, e.g.,in what is known as the cloud. Further, real time incident 110 andsensor data 116 may be stored as historic data, e.g., 112, as it isprovided directly to the forecast computer 102 and Dynamic Selector ordynamic selection module 104. Similarly, the Dynamic Selector 104 may beprovided by the forecast computer 102 or separately provided.

FIG. 2 shows a more detailed example of dynamically refining weatherforecasts 120 according to a preferred embodiment of the presentinvention, e.g., using the system 100 of FIG. 1, with like featureslabeled identically. After initially defining a large forecast area 122,the forecast system 102 superimposes 124 a coarse grid over on theforecast area 122. The forecast system 122 makes a quick forecast 114 ofthe weather for each cell area of the coarse grid. Once the dynamicselection module 104 has marked every cell complete, the quick forecast114 is sufficient 128, and analysis is complete 130.

If, however, the quick forecast 114 is insufficient 128, the dynamicselection module 104 provides 132 the forecast system 102 with a refinedgrid for selected cells, i.e., for locally better forecast resolution.The forecast system 102 superimposes 134 a corresponding refined grid onthe selected, remaining cells and makes 126 a refined forecast 114 foreach of those cells. Again, the dynamic selection module 104 checks eachcell of the refined gridded areas, marking those complete wheredemographics and weather allow. If the dynamic selection module 104marks all refined cells complete, the forecast 114 is sufficient 128 forall the refined cells, analysis is complete 130. Otherwise, the forecastsystem 102 continues using increasingly refined granularity 132 from thedynamic selection module 104 for increasingly smaller areas to arrive130, iteratively, at an acceptable 128 forecast 114.

So, for a given wide area 122, e.g., a continent or a country, aninitial N by M coarse grid is selected, either manually arbitrarilyselecting values for N and M or by default. The values of N and M areunimportant, other than to provide a starting point for the firstiteration. Typically, however, N and Mare selected such that the cellsare square and the product (N×M) is at least equal to the number offorecasting computers 102. Preferably, N and Mare selected or setinitially, such that at that coarse resolution, the system 100 quicklydelivers 126 a forecast 114 that lacks very rich details.

The forecast system 102 provides the first, quick forecast 114 using theN by M coarse grid superimposed on mid or low resolution map(s), e.g.,an area map at the lowest available resolution from geodatabase 118, incombination with the meteorological sensor data 116. Preferably, theforecast system 102 forecasts 126 cell weather using a numerical modelsolver, e.g., a model based on computer fluid dynamics. The WeatherResearch and Forecasting (WRF) model is an example of forecastingweather based on computer fluid dynamics. See, e.g., IBM's Deep Thunderwww.research.ibm.com/weather/DT.html for one such WRF model.

Then, the forecast system 102 passes the initial forecast 114 to thedynamic selection module 104. The dynamic selection module 104 markscomplete unpopulated and uneventful coarse cells, and coarse cells withfavorable or mild weather, and the coarse-resolution analysis issufficient 128 for areas in the marked cell(s). The remaining, unmarkedcoarse cells have conditions during the forecast period that requirefurther analysis, e.g., rainstorms or hurricanes, or other events,scheduled or otherwise. For example, some unmarked cells may have ahistory of fires during drought or landslides during rain. In anyiteration, when all of the cells are marked as complete 128, additionalaction is unnecessary; and, the dynamic selection module 104 notifiesthe forecast system 102 that the forecast is complete 130.

Alternately, the dynamic selection module 104 may use event data 106,demographic data 108 and real-time incidents 110 to weight variables indeciding about whether additional refinement of any cell is needed ornot. The decision may be automatic, e.g., if the weighted responseexceeds a threshold, or manual in response to input from a human.

Until all cells are marked complete 130, however, some cells remain thatenclose, e.g., inclement weather in heavily populated areas, or inuninhabited or sparsely populated venues for scheduled events or with ahistory of problems. The dynamic selection module 104 selects an I by Kgrid, where I and K also are arbitrarily selected, for a higherresolution forecast for the remaining areas.

The dynamic selection module 104 passes the higher resolution grid 132to the forecast system 102, which begins the next iteration 126. In thisand each subsequent iteration, the forecast system 102 applies therefined grid to higher resolution local maps from geodatabase 118 forthose previously unmarked cells. Then, the forecast system 102 generatesa forecast 114 for each refined grid cell based on available informationin event data 106, demographic data 108, real-time incident 110 andhistorical incident data 112, forecast data 114 and real-timemeteorological sensor data 116. Preferably, the forecast system 102 usesa numerical model solver on the sensor data 116 to provide 126 a refinedforecast 114 for each cell in the newly gridded areas.

With each new refined forecast 114, the dynamic selection module 104further reduces the area being considered by marking complete anyrefined cells with no predicted activity. If there are unmarked cellsafter marking, those cells are further refined 132 in the next iterationusing the next highest map resolution. Refining the grid and forecasting114 continues until the dynamic selection module 104 finds an unchanged,identified final resolution or until the forecast is provided for thehighest resolution map for an unmarked area and no further refinement isavailable. At that point the cell is marked complete 128. After all ofthe cells are marked complete 128, additional action is unnecessary andthe forecast is complete 130.

FIG. 3 shows a simple example of refining a forecast for a wide area140, wherein for local analysis and forecasting, grid refinement ispopulace centric according to a preferred embodiment of the presentinvention; and with further reference to the system 100 of FIG. 1 andmethod 120 of FIG. 2. In the initial iteration a three by three (3×3)square cell grid is overlaid on wide area 140. After the initialforecast 114, subsequent iterations are populace centric.

In this example, the dynamic selection module 104 begins by selectingone cell and gathers demographic data 108 about the inhabitants andtransient population in the area contained in the selected cell. If thedemographic analysis indicates has clear or otherwise stable weatherduring the forecast or that the selected cell is deserted and has noscheduled events, for example, that cell 142 is marked complete.Otherwise, for example, unmarked cells may contain cities, villages orsome other human activity, static or transient. The dynamic selectionmodule 104 determines that these areas need further examination andrefines the grid for those areas in a population centric refinement.

So after the coarse forecast in this example, six (6) cells 142 aremarked complete, e.g., uninhabited areas that may even be experiencingheavy rain or densely populated areas with clear, balmy weather. Theremaining three (3) cells 144, 146, 148 contain inclement weather andsignificant population or have scheduled or unscheduled events, e.g., ahistory of weather related events. The dynamic selection module 104marks all but those 3 cells 144, 146, 148, which remain unmarked for amore comprehensive forecast.

With each iteration, the dynamic selection module 104 can prioritizeunmarked areas by weighting information from static data (e.g., fixedpopulation and scheduled events) and dynamic data (e.g., transientpopulation and events that have a history of occurring during inclementweather) to grid the areas and forecast weather for increasingly smallergeographic regions. For example, the dynamic selection module 104 canidentify and give higher priority to coarse cells with storm activity inpopulated areas, i.e., weather that may threaten demographically denseregions located in those coarse cells. With each iteration the forecast114 is complete for more area, as areas where the weather is likely tohave little impact or, where a higher resolution forecast isunavailable, are marked complete. Some marked unpopulated areas may evenhave significant weather, for example, storms that are in areas wherepeople are likely to be unaffected.

After the first iteration the dynamic selection module 104 generates aset of sub-regions only for each unmarked cell, i.e., a locally higherresolution grid. So, a smaller 3×3 grid is applied to cells 144, 146,148. The refined grid is returned to the forecast system 102 forrefining 126 the forecast 114 for the area of cells 144, 146, 148 withbetter confidence. After this second iteration, refined grid cells 150in cell 146 are marked complete. Thus, cell 146 is also complete becauseeach cell 150 in cell 146 either lacks significant population whereweather is inclement or, because the local forecast shows fair weatherin cells 150 where people reside.

Only 2 of the refined grid cells 152 remain unmarked after the refinedforecast 114 of the second iteration. These remaining cells 152, mostlikely, have densely populated locations with heavy weather or havemoderately populated locations with scheduled events or a history ofweather events even in moderate weather (e.g., frequent mud slides).Alternately, the dynamic selection module 104 may simultaneously locateheavily populated locations in all unmarked cells and grid those cellsfirst in a refinement 132. In the next and final iteration of thisexample, the dynamic selection module 104 applies a still smaller 3×3grid to cells 152. Then, the forecast system 102 retrieves the nexthighest resolution map from geodatabase 118 and applies the refined grid134 to the higher resolution map. The forecast system 102 uses thishighest resolution grid and map to refine the forecast 114 for thosecells 152 to complete the population centric forecast.

A preferred system 100 can use all those information for various localareas to trigger a nested and fine grain resolution Weather Forecast forthat/those areas. Unlike prior methods that rely solely on staticinformation or on where cloud formation locations, the preferredpopulace centric forecast system 100 provides weather forecasts,tailored to the dynamic nature of the local populace. These populacecentric forecasts efficiently leverage system facilities, selectivelyproviding high resolution results only as and where necessary; whileotherwise minimizing resources used in providing forecasts to areasdetermined to be less critical or less important. Thus, a preferredsystem 100 optimizes computing resource usage leading to a greener datacenter and insuring considering the dynamic characteristics of localpeople and cities in complete region forecasts.

For 2 separate occasions of inclement weather, for example, anamphitheater, may be gridded initially in the same coarse cell. Aconcert is scheduled in the amphitheater during the first forecastperiod and the amphitheater is empty during the second. Thus, the coarsecell from the first pass or iteration is refined for at least onesubsequent iteration to provide a forecast for concertgoers. In thesecond forecast iteration, however, because people are elsewhere,weather is unimportant for the amphitheater. The initial coarse cellforecast is sufficient. Thus, a preferred system 100 dedicates systemresources to providing the detailed forecast for the concert; and, freesmuch of those resources for use elsewhere when a detailed forecast isunnecessary.

Thus advantageously, the preferred system 100 can provide a verylocalized and targeted, but robust forecast by considering all availableinformation. The preferred system 100 uses both dynamic information,e.g., event venue (concerts, parties, races, tournaments) schedules,area wide traffic patterns and population fluidity, and historicallyhazardous (landslides and floods) areas; and static information, e.g.,high demographic concentrations. Moreover, from this information, thepreferred system 100 verifies which places are scheduled to host, or arecurrently hosting, special events; assesses which local areas arehistorically considered dangerous or susceptible to natural disasters;and identifies densest locations in the forecast area.

These different types of information may be used to eliminate coarsercells where weather is less or less likely to impact the local populacefrom an increasingly local area; and focus with increasing granularitythrough more and more refined, higher resolution cells where moredetailed, and more comprehensive forecasts are desired. Further, dynamicinformation may be collected on transient population, for example, fromcell phone providers detecting geolocation of anonymous people in remotelocations, traffic sensors detecting traffic jams or intense traffic ofvehicles and people, currently scheduled incidents taking place inspecific regions.

While the invention has been described in terms of preferredembodiments, those skilled in the art will recognize that the inventioncan be practiced with modification within the spirit and scope of theappended claims. It is intended that all such variations andmodifications fall within the scope of the appended claims. Examples anddrawings are, accordingly, to be regarded as illustrative rather thanrestrictive.

What is claimed is:
 1. A populace centric weather forecast systemcomprising: a forecasting computer applying a grid to a forecast areaand providing a weather forecast for each grid cell; one or more areaactivity data sources indicating human activity in said forecast area;and a dynamic selection module iteratively identifying grid cells forrefinement responsive to said weather forecast and indicated said humanactivity, said dynamic selection module providing said forecastingcomputer with a refined grid for each identified said grid cell in eachiteration, said forecasting computer providing a more comprehensiveweather forecast in said each iteration.
 2. A populace centric weatherforecast system as in claim 1, wherein said one or more area activitydata sources include historical data, real time data and projected data.3. A populace centric weather forecast system as in claim 2, whereinsaid one or more area activity data sources include event data,demographic data, historical incident data, weather sensor data andgeodata.
 4. A populace centric weather forecast system as in claim 3,wherein said demographic data includes real time demographic data andsaid weather sensor data includes real time sensor data.
 5. A populacecentric weather forecast system as in claim 1, wherein in each iterationsaid dynamic selection module identifies as complete any cells in themost recent iteration with weather inactivity or human inactivity duringthe forecast period.
 6. A populace centric weather forecast system as inclaim 5, wherein said dynamic selection module ignores cell areaspreviously identified complete in providing refined grids.
 7. A populacecentric weather forecast system as in claim 6, wherein in each saiditeration said forecasting computer overlays a current grid on an areamap from a geodatabase and forecasts weather for each grid cellresponsive to real time weather sensor data.
 8. A method of forecastingweather, said method comprising: overlaying an initial grid on aforecast area, said initial grid segmenting said forecast area intocells; forecasting the weather during a forecast period for the areaenclosed in each cell; marking complete each cell with weather or humaninactivity; determining if all cells are marked complete; providing arefined grid for every cell not marked complete; overlaying said refinedgrid on a respective forecast area not marked complete; and returning toforecasting the weather, wherein in the next iteration the weather isforecast for each area enclosed in each refined grid cell.
 9. A methodof forecasting weather as in claim 8, wherein when all cells are markedcomplete, the weather forecast for said forecast area is complete.
 10. Amethod of forecasting weather as in claim 8, wherein marking cellscomplete comprises: receiving activity data for each cell from activitydata sources; and determining for said each cell whether the currentforecast and said activity data indicate weather inactivity or humaninactivity for the respective cell area during said forecast period. 11.A method of forecasting weather as in claim 10, wherein said activitydata includes historical data, real time data and projected data.
 12. Amethod of forecasting weather as in claim 10, wherein said activity dataincludes event data, demographic data, historical incident data, weathersensor data and geodata.
 13. A method of forecasting weather as in claim8, wherein cells marked complete include uninhabited cells withinclement weather forecast and heavily populated cells with clearweather forecast.
 14. A method of forecasting weather as in claim 8,wherein overlaying said initial grid on said forecast area comprisesoverlaying said initial grid on a map of said forecast area having afirst resolution, said refined grid being overlaid on a higherresolution map of said respective cell area.
 15. A method of forecastingweather as in claim 14, wherein cells are further marked completewhenever a forecast has been provided for said respective forecast areawith said refined grid overlaid on the highest resolution map.
 16. Acomputer program product for forecasting weather, said computer programproduct comprising a computer usable medium having computer readableprogram code stored thereon, said computer readable program code causinga computer executing said code to: overlay an initial grid on a forecastarea, said grid segmenting said forecast area into cells; anditeratively forecast the weather for the area enclosed in each cell,said forecast being for a forecast period; mark as complete each cellwith weather inactivity or human inactivity during said forecast period;identify any cells not marked complete; provide a refined grid for thearea in every unmarked cell; overlay said refined grid on a respectiveunmarked forecast area; and return to forecast the weather in a nextiteration, wherein the weather is forecast for each area enclosed ineach refined grid cell.
 17. A computer program product for forecastingweather as in claim 16, wherein overlaying said initial grid on saidforecast area comprises retrieving a map of said forecast area having afirst resolution and overlaying said initial grid on said map.
 18. Acomputer program product for forecasting weather as in claim 17, whereinoverlaying said refined grid comprises retrieving a higher resolutionmap of said respective forecast area, said refined grid being overlaidon said higher resolution map, and wherein said cells in said refinedgrid are further marked complete whenever a forecast has been providedfor the highest resolution map for said respective forecast area.
 19. Acomputer program product for forecasting weather as in claim 16, whereinconditions in said forecast area are modeled with a fluid dynamics modelto forecast the weather and whenever all cells are marked complete, theweather forecast for said forecast area is complete for said forecastperiod.
 20. A computer program product for forecasting weather as inclaim 13, wherein marking cells comprises determining whetherhistorical, real time and projected activity data sources indicateweather activity and human activity during said forecast period in saidforecast area, said activity data sources including sources of eventdata, demographic data, historical incident data, forecast data, weathersensor data and geodata.
 21. A computer program product for forecastingweather in a populace centric weather forecast, said computer programproduct comprising a computer usable medium having computer readableprogram code stored thereon, said computer readable program codecomprising: computer readable program code means for applying a grid toa forecast area; computer readable program code means for providing aweather forecast for each grid cell in said forecast area; computerreadable program code means for indicating weather and human activity insaid forecast area; computer readable program code means for identifyinggrid cells for refinement responsive to indicated said weather and humanactivity in each grid cell; and computer readable program code means forproviding a refined grid for said each identified said grid cell, saidcomputer readable program code means for applying said grid applyingsaid refined grid, a more comprehensive said weather forecast beingprovided for identified said grid cells in the next iteration, apopulace centric weather forecast being iteratively provided.
 22. Acomputer program product for forecasting weather in a populace centricweather forecast as in claim 21, wherein said computer readable programcode means for indicating weather and human activity comprises computerreadable program code means for storing event data, demographic data,historical incident data, forecast data and a geodatabase.
 23. Acomputer program product for forecasting weather in a populace centricweather forecast as in claim 22, further comprising computer readableprogram code means for receiving real time demographic data and weathersensor data.
 24. A computer program product for forecasting weather in apopulace centric weather forecast as in claim 22, wherein said eventdata includes data on historical events and scheduled events.
 25. Acomputer program product for forecasting weather in a populace centricweather forecast as in claim 22, wherein said computer readable programcode means for indicating weather and human activity identifies ascomplete any cells in the most recent iteration with weather or humaninactivity, previously identified complete cell areas being ignored inproviding weather for each said refined grid and a current grid beingoverlaid on an area map from said geodatabase, weather being forecastfor each grid cell responsive to real time sensor data.